Evidence map›Paper›PMID 42740237›Full record

Trial reportSensors (Basel, Switzerland)2026

EEG vs. Hybrid EEG-fNIRS BCI for FES Control in Healthy Subjects: A Blind Randomized Study and an Open Dataset.

Olesya Mokienko, Evgeniy Lukyanov, Leonid Kim, Mikhail Isaev, Gregory Gabuzov, Dmitry Bobrov, Roman Lyukmanov, Natalia Suponeva, Ksenia Ustinova, Pavel Bobrov

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Olesya MokienkoDepartment of Neuro-computer Interfaces, Pirogov Russian National Research Medical University, Moscow 117513, Russia.ORCID 0000-0002-7826-5135
Evgeniy LukyanovDepartment of Neuro-computer Interfaces, Pirogov Russian National Research Medical University, Moscow 117513, Russia.ORCID 0009-0007-5672-1025
Leonid KimInstitute of Biomedical Engineering, University of Science and Technology "MISIS", Moscow 119049, Russia.
Mikhail IsaevDepartment of Neuro-computer Interfaces, Pirogov Russian National Research Medical University, Moscow 117513, Russia.ORCID 0000-0002-3907-5056
Gregory GabuzovDepartment of Neuro-computer Interfaces, Pirogov Russian National Research Medical University, Moscow 117513, Russia.ORCID 0000-0003-1511-3692
Dmitry BobrovDepartment of Neuro-computer Interfaces, Pirogov Russian National Research Medical University, Moscow 117513, Russia.ORCID 0000-0003-3077-5416
Roman LyukmanovInstitute for Neurorehabilitation and Restorative Technologies, Russian Center of Neurology and Neurosciences, Moscow 125310, Russia.ORCID 0000-0002-8671-5861
Natalia SuponevaInstitute for Neurorehabilitation and Restorative Technologies, Russian Center of Neurology and Neurosciences, Moscow 125310, Russia.
Ksenia UstinovaDepartment of Physical Therapy, Central Michigan University, Mount Pleasant, MI 48859, USA.ORCID 0000-0002-1531-7591
Pavel BobrovDepartment of Neuro-computer Interfaces, Pirogov Russian National Research Medical University, Moscow 117513, Russia.ORCID 0000-0003-2566-1043

Funding

Ministry of Health of the Russian Federation 125022602910-2.
6 · The paper itself

Abstract

Among the various brain-computer interface (BCI) modifications used in post-stroke rehabilitation, BCI systems combined with functional electrical stimulation (FES) are considered the most effective. As a preliminary step toward optimizing such systems for clinical application, it remains unclear whether using electroencephalography (EEG) alone versus a hybrid EEG and functional near-infrared spectroscopy (fNIRS) approach affects real-time three-class BCI-FES control performance in healthy individuals. In a blind randomized study, 16 healthy volunteers completed five BCI-FES training sessions across three days. In one group, FES of wrist extensor muscles was driven by a hybrid EEG-fNIRS classifier; in the other, by EEG only. Classification accuracy, sense of agency, attention, and physical comfort were assessed. No statistically significant between-group differences were found in any outcome measure (

Indexed as

Brain-Computer InterfacesElectric StimulationElectroencephalographyAdultAlgorithmsFemaleHealthy VolunteersHumansMaleSpectroscopy, Near-InfraredYoung Adultbrain–computer interfaceelectric stimulationelectroencephalographynear-infrared spectroscopy

Identifiers

PMID42740237
PMCPMC13568455

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.